In this first chunk, let’s read in the Census microdata. Here is some example code on how to read in the data, create new variables to categorize the rows of data into groups, and then summarize the data to create information about Louisville.
Our goal is to create variables for gender, age group, whether someone is a mother, whether someone is married, their level of education, their income, whether they are the head of household, and the number of children they have.
This code chunk will identify which households are homeowners vs. renters (in the homeownership variable) and which households are cost-burdened, meaning they pay more thatn 30% of their income toward rent or a mortgage (in the cost_burden variable).
There are also variables for severe cost burden (households that pay more than half of their income towards housing) and households with severe housing problems (lacking a kitchen, adequate plumbing, or an ample number of rooms for the number of people living there).
load("clean_svybydemog_data.RData")
#Waffle Chart
temp_df <- H_earntype %>%
filter(race == 'total',
var_type == "percent", sex == "total") %>%
pivot_wider(names_from = "earner_type_d", values_from = "homeownership")
trend(temp_df,
multiple_earner:single_male_earner,
plot_title = "Homeownership by Year",
cat = c("Multiple Earners" = "multiple_earner", "Single Female" = "single_fem_earner", "Single Male" = "single_male_earner"),
pctiles = F,
y_title = 'Percent',
rollmean = 3,
caption_text =
"Source: Greater Louisville Project
Data from GLP analysis of ACS microdata from IPUMS-USA")
temp_df <- H_earntype %>%
filter(earner_type_d == "single_fem_earner",
var_type == "percent", sex == "total") %>%
mutate(sex = "total")
ranking(temp_df,
'homeownership',
plot_title = "Single Earner Female Homeownership",
caption_text =
"Source: Greater Louisville Project
Data from GLP analysis of ACS microdata from IPUMS-USA")
H_s_Femkids_trend %<>%
filter(
var_type == 'percent',
race == 'total',
sex == "female") %>%
pivot_wider(names_from = 'kd_pres', values_from = 'homeownership') %>%
select(-sex)
trend(H_s_Femkids_trend,
kids:no_kids,
rollmean = 3,
plot_title = "Female Homeownership by Presence of Children",
cat = c("Children" = "kids", "No Children" = "no_kids"),
y_title = 'Percent',
caption_text =
"Source: Greater Louisville Project
Data from GLP analysis of ACS microdata from IPUMS-USA")
ranking(H_sinFem_kids,
'homeownership',
plot_title = "Single Earner Female Homeownership with Children",
#title_scale = 0.8,
caption_text =
"Source: Greater Louisville Project
Data from GLP analysis of ACS microdata from IPUMS-USA")
temp_df <- H_earntype %>%
filter(earner_type_d == "single_fem_earner",
var_type == "percent", sex == "total")
trend(filter(temp_df, race != "hispanic"),
homeownership,
rollmean = 3,
pctiles = F,
plot_title = "Single Female Homeownership by Year",
cat = 'race',
y_title = 'Percent',
caption_text =
"Source Greater Louisville Project
Data from GLP analysis of ACS microdata from IPUMS-USA")
df_kids_race <- census_microdata081122 %>%
group_by(FIPS, year, race, earner_type_d, kd_pres) %>%
summarize(homeownership = sum(HHWT[homeownership]) / sum(HHWT) * 100, .groups = "drop")
df_kids <- census_microdata081122 %>%
group_by(FIPS, year, earner_type_d, kd_pres) %>%
summarize(homeownership = sum(HHWT[homeownership]) / sum(HHWT) * 100, .groups = "drop") %>%
mutate(race = "total")
df_kids %<>%
bind_rows(df_kids_race) %>%
select(FIPS, year, race, earner_type_d, homeownership, kd_pres) %>%
filter(earner_type_d == "single_fem_earner",
kd_pres == "kids")
trend(filter(df_kids, race != "hispanic"),
homeownership,
rollmean = 3,
pctiles = F,
plot_title = "Single Female Homeownership by Year with Children",
cat = 'race',
y_title = 'Percent',
caption_text =
"Source Greater Louisville Project
Data from GLP analysis of ACS microdata from IPUMS-USA")
df_no_kids_race <- census_microdata081122 %>%
group_by(FIPS, year, race, earner_type_d, kd_pres) %>%
summarize(homeownership = sum(HHWT[homeownership]) / sum(HHWT) * 100, .groups = "drop")
df_no_kids <- census_microdata081122 %>%
group_by(FIPS, year, earner_type_d, kd_pres) %>%
summarize(homeownership = sum(HHWT[homeownership]) / sum(HHWT) * 100, .groups = "drop") %>%
mutate(race = "total")
df_no_kids %<>%
bind_rows(df_no_kids_race) %>%
select(FIPS, year, race, earner_type_d, homeownership, kd_pres) %>%
filter(earner_type_d == "single_fem_earner",
kd_pres == "no_kids")
trend(filter(df_no_kids, race != "hispanic"),
homeownership,
rollmean = 3,
pctiles = F,
plot_title = "Single Female Homeownership by Year without Children",
cat = 'race',
y_title = 'Percent',
caption_text =
"Source Greater Louisville Project
Data from GLP analysis of ACS microdata from IPUMS-USA")
#fix formatting
single_earner_pctiles <- lville_2019 %>%
group_by(sex) %>%
summarize(
ten_pct = Hmisc::wtd.quantile(HHINCOME, HHWT, probs = 0.1),
twenty_five_pct = Hmisc::wtd.quantile(HHINCOME, HHWT, probs = 0.25),
fifty_pct = Hmisc::wtd.quantile(HHINCOME, HHWT, probs = 0.5),
seventy_five_pct = Hmisc::wtd.quantile(HHINCOME, HHWT, probs = 0.75),
ninety_pct = Hmisc::wtd.quantile(HHINCOME, HHWT, probs = 0.9))
library(gt)
gt(single_earner_pctiles) %>%
tab_header(title = "Income Percentiles by Sex",
subtitle = "") %>%
fmt_currency(columns = vars(ten_pct, twenty_five_pct, fifty_pct, seventy_five_pct,
ninety_pct),
use_subunits = F) %>%
cols_label(ten_pct = "10th",
twenty_five_pct = "25th",
fifty_pct = "Median",
seventy_five_pct = "75th",
ninety_pct = "90th") %>%
cols_align(align = "center") %>%
tab_source_note(
source_note = md("Source: ACS microdata from IPUMS-USA")) %>%
opt_row_striping(row_striping = TRUE) %>%
opt_table_outline() %>%
tab_options(
table.font.size = px(12),
table.width = pct(50)) %>%
tab_style(
cell_text(
font = "Montserrat",
weight = "bold"),
cells_row_groups())
| Income Percentiles by Sex | |||||
| sex | 10th | 25th | Median | 75th | 90th |
|---|---|---|---|---|---|
| female | $11,200 | $25,000 | $50,000 | $91,000 | $152,000 |
| male | $18,000 | $37,000 | $66,900 | $108,400 | $170,000 |
| Source: ACS microdata from IPUMS-USA | |||||
p <- lville_2019 %>%
filter(HHINCOME <= cut_95,
earner_type == "single_earner") %>%
func_plt_hist_overlay( "sex")
p <- p + glp_graph_theme
p <- p + labs(
title = "Single Earner Income by Gender",
) +
ylab(" ") +
guides(color = FALSE) +
facet_wrap(~sex, nrow = 2) +
theme(
#axis.ticks.x = element_line(size = 50000),
strip.text = element_blank()
) +
scale_x_continuous(
breaks = c(50000, 100000, 150000, 200000),
label = c("$50k", "$100k", "$150k", "$200k")
) +
scale_y_continuous(labels = scales::comma)
p
temp_df <- lville_2019 %>%
filter(HHINCOME <= cut_95,
earner_type == "single_earner")
p_percent <- ggplot(temp_df, aes(x=HHINCOME,
y = (..count..)/sum(..count..),
fill=sex,
color = sex,
weight = HHWT)) +
geom_histogram(alpha=0.5, position = 'identity', binwidth = 10000) +
scale_fill_manual(values = c("#0E4A99", "#F58021", "#00A9B7")) +
scale_color_manual(values = c("#0E4A99", "#F58021", "#00A9B7")) +
labs(fill="") +
xlab("Household Income") +
ylab("Percentage")
p_percent <- p_percent + glp_graph_theme
p_percent <- p_percent + labs(
title = "Single Earner Income by Gender",
) +
ylab(" ") +
guides(color = FALSE) +
facet_wrap(~sex, nrow = 2) +
theme(
#axis.ticks.x = element_line(size = 50000),
strip.text = element_blank()
) +
scale_x_continuous(
breaks = c(50000, 100000, 150000, 200000),
label = c("$50k", "$100k", "$150k", "$200k")
) +
scale_y_continuous(labels=percent)
p_percent
##add original faceted graph
sing_fem_inc_race<-census_microdata081122 %>%
filter(
FIPS == "21111",
year %in% 2016:2019,
sex == 'female',
earner_type == 'single_earner',
HHINCOME <= cut_95)
sing_fem_inc_race_plt <- sing_fem_inc_race %>%
ggplot( aes(x=HHINCOME,
y = (..count..)/sum(..count..),
fill=race,
color = race,
weight = HHWT)) +
geom_histogram(alpha=0.5, position = 'identity', binwidth = 10000)
sing_fem_inc_race_plt <- sing_fem_inc_race_plt + facet_wrap(~race, nrow = 2)
sing_fem_inc_race_plt <- sing_fem_inc_race_plt + glp_graph_theme
sing_fem_inc_race_plt <- sing_fem_inc_race_plt +
labs(
title = "Female Single Earner Income",
) +
ylab(" ") +
xlab("Household Income")
# guides(color = FALSE)
sing_fem_inc_race_plt <- sing_fem_inc_race_plt +
theme(
#axis.ticks.x = element_line(size = 50000),
strip.text = element_blank()
) +
scale_x_continuous(
breaks = c(50000, 100000, 150000),
label = c("$50k", "$100k", "$150k")
) +
scale_y_continuous(labels = scales::percent)
sing_fem_inc_race_plt <- sing_fem_inc_race_plt +
scale_fill_manual(values = c("#0E4A99", "#F58021","#00A9B7", "#800055")) +
scale_color_manual(values = c("#0E4A99","#F58021","#00A9B7", "#800055"))
sing_fem_inc_race_plt
#need to create four separate tabs...one for each race...using % y axis
black_female_earner <- func_income_by_race("black")
black_female_earner
hisp_female_earner <- func_income_by_race("hispanic")
hisp_female_earner <- hisp_female_earner +
labs(
title = "Hispanic Female Single Earner Income",
) +
scale_fill_manual(values = "#0E4A99") +
scale_color_manual(values = "#0E4A99")
hisp_female_earner
white_female_earner <- func_income_by_race("white")
white_female_earner <- white_female_earner +
labs(
title = "White Female Single Earner Income",
) +
scale_fill_manual(values = "#F58021") +
scale_color_manual(values = "#F58021")
white_female_earner
other_female_earner <- func_income_by_race("other")
other_female_earner <- other_female_earner +
labs(
title = "Other Female Single Earner Income",
) +
scale_fill_manual(values = "#00A9B7") +
scale_color_manual(values = "#00A9B7")
other_female_earner
func_income_by_kids <- function(num_kids, living_wage) {
w <- census_microdata081122 %>%
filter(
FIPS == "21111",
year %in% 2016:2019,
sex == 'female',
NCHILD == num_kids,
earner_type == 'single_earner',
HHINCOME <= cut_95)
w <- w %>%
ggplot( aes(x=HHINCOME,
y = (..count..)/sum(..count..),
fill = sex,
group = sex,
weight = HHWT)) +
geom_histogram(alpha=0.5, position = 'identity', binwidth = 10000) +
geom_vline( aes(xintercept = living_wage), linetype = "dashed", colour="blue", size = 1.5)
#sing_fem_inc_race_plt <- sing_fem_inc_race_plt + facet_wrap(~race, nrow = 2)
w <- w + glp_graph_theme
w <- w +
labs(
title = "Black Female Single Earner Income",
) +
ylab(" ") +
xlab("Household Income")+
guides(color = FALSE)
w <- w +
theme(
#axis.ticks.x = element_line(size = 50000),
strip.text = element_blank()
) +
scale_x_continuous(
breaks = c(50000, 100000, 150000),
label = c("$50k", "$100k", "$150k")
) +
scale_y_continuous(labels = scales::percent)
return (w)
}
#why is color not working?
#still need to add living wage lines
under_liv_wage_0 <- census_microdata081122 %>%
filter(
FIPS == "21111",
year %in% 2016:2019,
sex == 'female',
NCHILD == 0,
earner_type == 'single_earner') %>%
group_by(HHINCOME < 30303.98) %>%
summarize(count = sum(HHWT)) #a little more than half are earning a living wage
#do this for each graphof this type...add info above chunk
no_kids_female_earner <- func_income_by_kids(0, 30303.98)
no_kids_female_earner <- no_kids_female_earner +
labs(
title = "Female Single Earner Income, No Children",
) +
scale_fill_discrete(labels = "No Children")
no_kids_female_earner
under_liv_wage_1 <- census_microdata081122 %>%
filter(
FIPS == "21111",
year %in% 2016:2019,
sex == 'female',
NCHILD == 1,
earner_type == 'single_earner') %>%
group_by(HHINCOME < 60264.75) %>%
summarize(count = sum(HHWT))
one_child <- func_income_by_kids(1, 60264.75)
one_child <- one_child +
labs(
title = "Female Single Earner Income, One Child",
) +
scale_fill_manual(values = "#800055", labels = "One Child" ) +
scale_color_manual(values = "#800055")
one_child
under_liv_wage_2 <- census_microdata081122 %>%
filter(
FIPS == "21111",
year %in% 2016:2019,
sex == 'female',
NCHILD == 2,
earner_type == 'single_earner') %>%
group_by(HHINCOME < 76451.81) %>%
summarize(count = sum(HHWT))
two_child <- func_income_by_kids(2, 76451.81)
two_child <- two_child +
labs(
title = "Female Single Earner Income, Two Children",
) +
scale_fill_manual(values = "#356E39", labels = "Two Children") +
scale_color_manual(values = "#356E39")
two_child
under_liv_wage_3 <- census_microdata081122 %>%
filter(
FIPS == "21111",
year %in% 2016:2019,
sex == 'female',
NCHILD == 3,
earner_type == 'single_earner') %>%
group_by(HHINCOME < 101452.61) %>%
summarize(count = sum(HHWT))
three_child <- func_income_by_kids(3, 101452.61)
three_child <- three_child +
labs(
title = "Female Single Earner Income With Three Children",
) +
scale_fill_manual(values = "#CFB94C", labels = "Three Children") +
scale_color_manual(values = "#CFB94C")
three_child
these_labels <- paste0(dollar(seq(1, 273500, 10000), scale = 0.001, accuracy = 1, suffix = "k"))
cost_burden_sf <- lville_2019 %>%
filter(
sex == 'female',
earner_type == 'single_earner',
HHINCOME <= cut_95) %>%
mutate(
cost_burden = factor(cost_burden,
levels = rev(c(TRUE, FALSE)),
labels = rev(c("Cost Burdened", "Non Cost Burdened")),
ordered = TRUE),
inc_bins = cut(HHINCOME, seq(1, 283500, 10000),
labels = these_labels) %>%
factor(levels = these_labels, ordered = TRUE)
)
temp_df <- cost_burden_sf %>%
group_by(inc_bins, cost_burden) %>%
summarize(count = sum(HHWT), .groups = "drop") %>%
complete(inc_bins, cost_burden, fill = list(count = 0)) %>%
filter(!is.na(inc_bins)) %>%
group_by(inc_bins) %>%
mutate(percent = count / sum(count)) %>%
ungroup() %>%
filter(cost_burden == "Cost Burdened")
temp_df <- temp_df[1:14,]
cost_burden_sf_plot <- ggplot(temp_df,
aes(x = inc_bins,
y = percent,
group = 1)) +
geom_line(linetype = "dotted", color="purple", size=3) +
geom_point(color="purple", size=8)
cost_burden_sf_plot <- cost_burden_sf_plot + glp_graph_theme
cost_burden_sf_plot <- cost_burden_sf_plot +
labs(
title = "Female Single Earner Cost Burden Trend",
) +
ylab(" ") +
xlab("Household Income") +
guides(color = FALSE) +
theme(
strip.text = element_blank()
) +
scale_color_manual(values = c("#0E4A99")) +
scale_y_continuous(labels = scales::percent)
cost_burden_sf_plot
#I_CB_earn_trend <- survey_by_demog(census_microdata081122, weight_var = "HHWT", 'cost_burden', other_grouping_vars = c('earner_type'), breakdowns = "sex")
# I_CB_earn_trend <- survey_by_demog(census_microdata081122, weight_var = "HHWT", 'cost_burden', other_grouping_vars = c('earner_type_d'))
I_CB_earn_trend %<>%
filter(
var_type == 'percent',
race == 'total',
sex == 'total') %>%
select( -c(sex,race)) %>%
pivot_wider(names_from = "earner_type_d", values_from = "cost_burden")
trend(I_CB_earn_trend,
multiple_earner:single_fem_earner:single_male_earner,
pctiles = F,
plot_title = "Cost Burden by Earner Type",
cat = c("Multiple Earners" = "multiple_earner", "Single Female Earner" = "single_fem_earner", "Single Male Earner" = "single_male_earner"),
y_title = 'Percent',
caption_text =
"Source Greater Louisville Project
Data from GLP analysis of ACS microdata from IPUMS-USA")
I_median_earn_age <- lville_2019 %>%
group_by(age_group, earner_type_d) %>%
summarize(Med=median(HHINCOME))
I_median_earn_age_plot <- ggplot(I_median_earn_age,
aes(x=age_group, y=Med, fill = earner_type_d)) +
geom_bar(stat="identity", position='dodge')
I_median_earn_age_plot <- I_median_earn_age_plot + glp_graph_theme
I_median_earn_age_plot <- I_median_earn_age_plot +
labs(
title = "Median Earnings by Age Group",
) +
ylab("Household Income") +
xlab("Age Group") +
scale_y_continuous(labels = scales::dollar) +
scale_fill_manual(
values = c("#0E4A99", "#F58021", "#00A9B7"),
labels = c("Multiple Earner", "Single Female Earner", "Single Male Earner"))
I_median_earn_age_plot
#need to think about this more...maybe just use to focus on how similar ppl without kids are
E_singM_singF <- census_microdata081122 %>%
filter(year %in% 2017:2019,
earner_type == 'single_earner') %>%
group_by(sex, educ, kd_pres) %>%
summarize(n=sum(HHWT, na.rm = TRUE)) %>%
mutate(
total = sum(n),
rate = n/sum(n)*100,
educ = factor(educ,
levels = rev(c("no_hs", "hs", "some_col", "assoc", "bach","grad")),
ordered = TRUE))
E_singM_singF_plot <- ggplot(E_singM_singF,
aes(x=sex,
y=rate,
fill = educ)) +
geom_bar(stat="identity", position = "fill")
E_singM_singF_plot <- E_singM_singF_plot + facet_wrap(~kd_pres)
E_singM_singF_plot <- E_singM_singF_plot + glp_graph_theme
E_singM_singF_plot <- E_singM_singF_plot +
theme(
legend.position = "right"
) +
labs(
title = "Educational attainment by gender for single earners",
) +
ylab(" ") +
xlab(" ") +
scale_fill_discrete(labels = c("Graduate","Bachelor", "Associate", "Some College", "High School", "No High School")) +
scale_x_discrete (labels = c("female" = "Female", "male" = "Male")) +
scale_y_continuous(labels = scales::percent)
E_singM_singF_plot
E_singF_race <- lville_2019 %>%
filter(
sex == 'female',
earner_type == 'single_earner') %>%
group_by(race, educ) %>%
summarize(n=sum(HHWT, na.rm = TRUE)) %>%
mutate(
total = sum(n),
rate = n/sum(n)*100,
educ = factor(educ,
levels = rev(c("no_hs", "hs", "some_col", "assoc", "bach","grad")),
ordered = TRUE))
E_singF_race_plot <- ggplot(E_singF_race, aes(x=race, y=rate, fill=educ)) +
geom_bar(stat="identity", position='fill')
E_singF_race_plot <- E_singF_race_plot + glp_graph_theme
E_singF_race_plot <- E_singF_race_plot +
theme(
legend.position = "right"
) +
labs(
title = "Single Female Education Breakdown",
) +
ylab(" ") +
xlab("Race") +
scale_fill_discrete(labels = c("Graduate","Bachelor", "Associate", "Some College", "High School", "No High School")) +
scale_x_discrete (labels = c("female" = "Female", "male" = "Male")) +
scale_y_continuous(labels = scales::percent)
E_singF_race_plot
cost_burden_age_sf %<>% drop_na(cost_burden) #this will need to be run once and then left alone if tweaking graphs
cost_burden_age_sf_plot <- ggplot(cost_burden_age_sf,
aes(x=age_group, y=HHWT , fill=cost_burden),
color="#00A9B7") +
geom_bar(stat="identity", position='fill')
cost_burden_age_sf_plot <- cost_burden_age_sf_plot + glp_graph_theme
cost_burden_age_sf_plot <- cost_burden_age_sf_plot +
theme(
legend.position = "right"
) +
labs(
title = "Cost Burdened Status by Age",
) +
ylab(" ") +
xlab("Race") +
scale_fill_discrete(labels = c("Non Cost Burdened", "Cost Burdened")) +
#scale_x_discrete (labels = c("female" = "Female", "male" = "Male")) +
scale_y_continuous(labels = scales::percent)
cost_burden_age_sf_plot
#x-axis...not legible on Josh's comp
temp_df <- cost_burden_age_sf %>%
mutate(
age_group = case_when(
age %in% 15:19 ~ NA_character_,
age %in% 20:29 ~ "20-29",
age %in% 30:39 ~ "30-39",
age %in% 40:49 ~ "40-49",
age %in% 50:59 ~ "50-59",
age %in% 60:69 ~ "60-69",
age %in% 70:79 ~ "70-79",
age >= 80 ~ "80+"))
cost_burden_age_sf_facet_plt <- ggplot(temp_df,
aes(x=age_group, y=HHWT , fill=cost_burden),
color="#00A9B7") +
geom_bar(stat="identity", position='fill')+
facet_wrap(~earner_type_d)
cost_burden_age_sf_facet_plt <- cost_burden_age_sf_facet_plt + glp_graph_theme
cost_burden_age_sf_facet_plt <- cost_burden_age_sf_facet_plt +
theme(
legend.position = "right",
strip.text = element_text(size = 40)
) +
labs(
title = "Cost Burdened Status by Age and Earner Type",
) +
ylab(" ") +
xlab(" ") +
scale_fill_discrete(labels = c("Non Cost Burdened", "Cost Burdened")) +
scale_x_discrete(guide = guide_axis(n.dodge=2)) +
scale_y_continuous(labels = scales::percent)
cost_burden_age_sf_facet_plt
earner_trend <- census_microdata081122 %>%
mutate(
earner_type_d = case_when(
sex == 'female' & earner_type == 'single_earner' ~ 'single_fem_earner',
sex == 'male' & earner_type == 'single_earner' ~ 'single_male_earner',
earner_type == 'multi_earner' ~ 'multiple_earner')
) %>%
group_by(year, earner_type_d) %>%
summarize(n=sum(HHWT, na.rm = TRUE)) %>%
mutate(
total = sum(n),
rate = n/sum(n)*100)
earner_trend_plt <- ggplot(earner_trend,
aes(x=year, y=rate, fill=earner_type_d),
color="#00A9B7") +
geom_bar(stat="identity", position='fill')
earner_trend_plt <- earner_trend_plt + glp_graph_theme
earner_trend_plt <- earner_trend_plt +
theme(
legend.position = "right"
#strip.text = element_blank()
) +
labs(
title = "Earner Type Trend"
) +
ylab(" ") +
xlab(" ") +
scale_fill_discrete(labels = c("Multiple Earner", "Single Female Earner", "Single Male Earner")) +
scale_y_continuous(labels = scales::percent)
earner_trend_plt